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Record W2182810163 · doi:10.18632/oncoscience.102

Selumetinib produces a central core of apoptosis in breast cancer bone metastases in mice

2014· article· en· W2182810163 on OpenAlexafffund
Nicholas A. Bosma, Arvind K. Singla, Charlene M. Downey, Frank R. Jirik

Bibliographic record

VenueOncoscience · 2014
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
FundersAlberta Cancer Foundation
KeywordsSelumetinibApoptosisCancer researchBreast cancerKRASMedicineCancerMEK inhibitorInternal medicineBiologyMAPK/ERK pathwaySignal transductionCell biology

Abstract

fetched live from OpenAlex

Bone is a common site for metastatic colonization in patients with breast cancer, hence the importance of identifying new treatments for this disease. Preclinical studies of bone metastases have commonly employed MDA-MB-231 cells that possess an activated KRAS allele. While activating RAS mutations are relatively rare in human breast cancer, increased RAS-RAF-MEK pathway activity is common in high-grade breast cancers. To study the consequences of MEK inhibition on bone metastases stemming from the intra-cardiac injection of luciferase-expressing MDA-MB-231 cells in mice, we used the MEK inhibitor AZD6244 (Selumetinib). We found that AZD6244 treatment caused decreased tumor bioluminescence that was associated with cavitation of the bone metastases, owing to apoptosis of cells specifically within the central region of the bone lesions. Hypothesizing that the latter effect was due to the increased sensitivity of poorly perfused regions to pro-apoptotic stimuli, we found that the combination of serum deprivation and AZD6244 led to dramatic induction pf MDA-MB-231 apoptosis in vitro. Our results suggest that MEK inhibition may be a strategy for triggering cell death within the hypoperfused, oxygen and nutrient poor regions of tumors with activated RAS alleles.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.328
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2014
Admission routes2
Has abstractyes

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